Biomedical question answering presents significant challenges due to the complexity of biomedical language and the need for precise information retrieval. This study aims to improve the performance of a biomedical information retrieval system through a hybrid learning-to-rank framework. Specifically, we combine lexical (BM25) and semantic (BioBERT) representations to form hybrid inputs for RankFormer, a transformer-based ranking model. This hybrid representation captures both surface-level term matching and deep contextual understanding. Experiments conducted on the BioASQ dataset show that our approach achieves better ranking performance compared to the standalone lexical or neural baselines, reaching a MAP@10 of 0.9614 and an nDCG@10 of 0.9320. These results highlight the effectiveness of hybrid input representations in enhancing biomedical answer ranking.
Bessai-Mechmache et al. (Thu,) studied this question.